• Introduce some basic data science skills
• Demonstrate how to work effectively in cross-disciplinary teams
• Give an opportunity to learn and produce something worklife related

Solveig Bjørkholt (PhD in political science)

Sondre Elstad (PhD in economics)

Alex Moltzau (AI Policy and Ethics at NORA)

• What’s your name?
• What do you study?
• Have you used any R before?
• What would you like to get out of this course?

• Hacking: Problem solving
• An event where people come together for a set duration of time to solve problems.

Kind of like a festival!

Political science: Domain knowledge about power relations, institutions and broad analytical skills for studying social phenomena.

• Main course material
• R for Data Science
• Storytelling with data
• Text mining with R
• Machine Learning for Social Scientists

But consider these learning resources rather than syllabus. The goal here is to learn through creating, so use these resources to look up things you wonder about rather than read them from A to Z.

• Week 1: Team work, Github, R, RStudio and R Markdown. Presentations from stakeholders.

• Week 2: Data gathering and visualization.

• Week 3: Text analysis.

• Week 4: Machine learning.

• Week 5: Machine learning, iterative work, deployment and IT communication.

• Week 6: Finishing projects and final presentations.

1. An R Markdown report exploring the case (optionally including a dashboard)
2. A Github repository with reproducible code
3. A team presentation at the 4th of August for the stakeholders and others

• The course ends with a pass/fail grade.
• And a reward for the winner team.

• We are a very diverse group in terms of backgrounds, programming competencies and so forth
• To capitalize on that, we need to help each other out

The one-up awards: Two awards to the two students who make a particular effort to help others out.

On Friday the 1st of July

The agile manifesto:

• Individuals and interactions over processes and tools
• Working software over comprehensive documentation
• Customer collaboration over contract negotiation
• Responding to change over following a plan

• Minimum viable product (MVP): The least finished product you can make that will still allow you to test the functionality.

• Sprints: Short sessions of work (e.g. one week) with clearly defined tasks

• Stakeholders: Frequently talk to the people who are going to use your product – does it address their needs?

• Roles:

  • Product owner: Knows what the product should look like in the end
  • Team: Developers of the product
  • Scrum master: Facilitates the work

How to capitalize on each others’ strengths?

Find the test in Canvas.